Evidence map›Paper›PMID 41091835›Full record

ArticleAnnals of the American Thoracic Society2026

Oncologic and physiologic predictors of mortality after intensive care unit admission in patients with lung cancer.

Regina M Koch, Abdullah Ramzan, Cameron G Gmehlin, Aaron S Mansfield, Kaushal Parikh, Konstantinos Leventakos, Julian R Molina, Yadav Hemang

Abstract read
In one paragraph

Article in Annals of the American Thoracic Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Regina M KochDepartment of Internal Medicine, Mayo Clinic, Rochester, MN, United States.
Abdullah RamzanMayo Clinic Alix School of Medicine, Rochester, MN, United States.
Cameron G GmehlinDepartment of Internal Medicine, Mayo Clinic, Rochester, MN, United States.
Aaron S MansfieldDepartment of Medical Oncology, Mayo Clinic, Rochester, MN, United States.
Kaushal ParikhDepartment of Medical Oncology, Mayo Clinic, Rochester, MN, United States.
Konstantinos LeventakosDepartment of Medical Oncology, Mayo Clinic, Rochester, MN, United States.
Julian R MolinaDepartment of Medical Oncology, Mayo Clinic, Rochester, MN, United States.
Yadav HemangDepartment of Pulmonary Medicine and Critical Care, Mayo Clinic, Rochester, MN, United States.

Funding

Predicting and Preventing Acute Respiratory Distress Syndrome after Hematopoietic Stem Cell TransplantationK23HL151671 · NHLBI · MAYO CLINIC ROCHESTER · PI YADAV, HEMANG · 2021 to 2025
$813k
NHLBI NIH HHS K23 HL151671
6 · The paper itself

Abstract

rationaleCritically ill lung cancer patients are a growing, high-risk population. However, prognostic tools to guide intensive care unit (ICU) decision-making are limited.

objectivesTo identify oncologic and critical illness factors associated with 90-day mortality following ICU admission in patients with lung cancer.

methodsWe conducted a retrospective cohort study of lung cancer patients admitted to the ICU across Mayo Clinic Health System between 2018 and 2024. The primary outcome was 90-day mortality. Two complementary Cox proportional hazards models were developed: a focused model including cancer-specific variables (stage, time since diagnosis, time since last systemic therapy, prior thoracotomy, Eastern Cooperative Oncology Group [ECOG] performance status prior to admission, body mass index, cause of critical illness, code status at admission) and a full model that added broader clinical factors (age and Sequential Organ Failure Assessment [SOFA] score). We also evaluated discharge disposition and transitions in code status.

resultsAmong 528 patients, 90-day mortality was 58.7%. In the focused model (C-index 0.67), independent predictors of mortality included late-stage disease, cancer-specific reason for ICU admission, limitations in resuscitation orders, and ECOG >1. In the full model (C-index 0.95), only age and SOFA score remained significant. Among early-stage patients, recent targeted or combination therapy was associated with higher mortality, though this association may reflect underlying disease characteristics or treatment selection rather than direct treatment effects. Patients who changed code status during admission had high mortality (83%) and a low rate of discharge home (11%).

conclusionLung cancer patients admitted to ICU experience high short-term mortality, with outcomes shaped by both oncologic status and the severity of acute illness. A dual-model approach suggests a risk hierarchy: Cancer-specific factors inform baseline mortality risk with an ICU admission, but age and physiologic derangement are the dominant drivers of survival once critical illness develops. These findings support dynamic, multidisciplinary prognostication and underscore the need to integrate oncology and critical care expertise in ICU decision-making.

Indexed as

Intensive Care UnitsLung NeoplasmsAgedCritical IllnessFemaleHospital MortalityHumansMaleMiddle AgedOrgan Dysfunction ScoresPrognosisProportional Hazards ModelsRetrospective StudiesRisk Factorscritical care outcomesintensive care unitslung neoplasms

Identifiers

PMID41091835
PMCPMC13387068

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.